An Investigation of Methods for Visualising Highly Multivariate Datasets

Editorial Introduction

The date used by social scientists are frequently multivariate. In part,
this is a consequence of a need to characterise objects of interest, such as
people, houses and so on, as fully as possible, but it is also often a result
of a desire to capture concepts such as social class or intelligence and
overcrowding that do not permit easy measurement along one axis of variation.
In consequence, quantitative social science has a long history of using
statistical and mathematical transforms of data matrices such as factor and
principal component analysis to reduce the dimensionality of these data and
perhaps suggest appropriate constructs that might also be used to describe
individuals.

These techniques are not intrinsically visual, although the reprojection of
individual cases onto axes that define these constructs (for examples as
component scores) may well create data that can be visualized by any of the
standard techniques. There remains a need to develop appropriate alternative
visualizations for multidimensional data that are efficient in allowing the
detection of patterns in the multivariate data space. In this Case Study,
Chris Brunsdon, Stweart Fotheringham and Martin Charlton develop and illustrate
three alternative projections that can be applied to multivariate data.

It is interresting to note that although the static displays produces are in
themselves useful, they gain maximum utility when visualized in an interactive
environment.